In this work in progress, we adapt the Probabilistic U-Net (hereafter PUnet) architecture proposed by [1]} to work on galaxy images. We namely modify the loss function to deal with very unbalanced datasets and demonstrate that the Probabilistic Deep Learning model provides meaningful segmentation uncertainties while obtaining
competitive scores both in completeness and purity, even when trained from a unique ground truth.
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